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  <div class="headertitle"><div class="title">Tensor.cu</div></div>
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<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="preprocessor">#include &quot;<a class="code" href="_tensor_8cuh.html">NeuZephyr/Tensor.cuh</a>&quot;</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="preprocessor">#include &quot;NeuZephyr/utils.cuh&quot;</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="preprocessor">#include &quot;<a class="code" href="_operation_kernels_8cuh.html">NeuZephyr/OperationKernels.cuh</a>&quot;</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="preprocessor">#include &quot;NeuZephyr/NeuZephyrCudaErrorHandling.cuh&quot;</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="preprocessor">#include &quot;NeuZephyr/StreamManager.cuh&quot;</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="preprocessor">#include &quot;NeuZephyr/TensorOperations.cuh&quot;</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &lt;curand.h&gt;</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span> </div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespacenz_1_1data.html">nz::data</a> {</div>
<div class="foldopen" id="foldopen00039" data-start="{" data-end="};">
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ab8eaa8e06861a868b7df1a9ee0616a1a">   39</a></span>    std::ostream&amp; operator&lt;&lt;(std::ostream&amp; os, <span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; tensor) {</div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        tensor.<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a2b2309d5428331f2e6f88037bb123c8f">print</a>(os);</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>        <span class="keywordflow">if</span> (tensor._requires_grad) {</div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>            os &lt;&lt; <span class="stringliteral">&quot;Gradient: &quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>            tensor.<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a4b02ed4d2afec1ce75931201af181e14">printGrad</a>(os);</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        }</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>        <span class="keywordflow">return</span> os;</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>    }</div>
</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span> </div>
<div class="foldopen" id="foldopen00076" data-start="{" data-end="};">
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a1ae147fdd4255f7d148aef41e3e436a9">   76</a></span>    std::istream&amp; operator&gt;&gt;(std::istream&amp; is, <span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; tensor) {</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        <span class="keyword">auto</span>* data = <span class="keyword">static_cast&lt;</span>Tensor::value_type*<span class="keyword">&gt;</span>(malloc(tensor._size * <span class="keyword">sizeof</span>(Tensor::value_type)));</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; tensor._size; ++i) {</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>            is &gt;&gt; data[i];</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        }</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;float&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(tensor._data, data, tensor._size * <span class="keyword">sizeof</span>(Tensor::value_type),</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>                                                        cudaMemcpyHostToDevice);</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        free(data);</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <span class="keywordflow">return</span> is;</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>    }</div>
</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span> </div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>    <span class="comment">// Constructors</span></div>
<div class="foldopen" id="foldopen00088" data-start="{" data-end="}">
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ad0dda0efff93778cab46fd5aa708b983">   88</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad0dda0efff93778cab46fd5aa708b983">Tensor::Tensor</a>() :</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>        _size(0), _shape({0, 0, 0, 0}), _data(<span class="keyword">nullptr</span>), _grad(<span class="keyword">nullptr</span>), _requires_grad(<span class="keyword">false</span>) {</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>    }</div>
</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span> </div>
<div class="foldopen" id="foldopen00092" data-start="{" data-end="}">
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a6a3fc1e2d0b5154cdb4961679d0752af">   92</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad0dda0efff93778cab46fd5aa708b983">Tensor::Tensor</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">shape_type</a>&amp; shape, <span class="keyword">const</span> <span class="keywordtype">bool</span> requires_grad) <span class="comment">// NOLINT(*-pro-type-member-init)</span></div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        :</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        _size(shape.size()), _shape(shape), _requires_grad(requires_grad) {</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;_data, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;_grad, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>        }</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>            _grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        }</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    }</div>
</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span> </div>
<div class="foldopen" id="foldopen00104" data-start="{" data-end="}">
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ad65fa89fac9d72c92d34ace7e94610df">  104</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad0dda0efff93778cab46fd5aa708b983">Tensor::Tensor</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">shape_type</a>&amp; shape, value_type* data, <span class="keyword">const</span> <span class="keywordtype">bool</span> requires_grad, <span class="keyword">const</span> <span class="keywordtype">bool</span> host) :</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        _size(shape.size()), _shape(shape), _requires_grad(requires_grad) {</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;_data, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        <span class="keywordflow">if</span> (host) {</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_data, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>                                                                 cudaMemcpyHostToDevice);</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        }</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_data, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>                                                                 cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        }</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;_grad, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        }</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>            _grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        }</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>    }</div>
</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span> </div>
<div class="foldopen" id="foldopen00123" data-start="{" data-end="}">
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a18937864a9eb48eb91a5d82ebf9c010e">  123</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad0dda0efff93778cab46fd5aa708b983">Tensor::Tensor</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">shape_type</a>&amp; shape, <span class="keyword">const</span> std::initializer_list&lt;value_type&gt;&amp; data, <span class="keyword">const</span> <span class="keywordtype">bool</span> requires_grad) :</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        _size(shape.size()), _shape(shape), _requires_grad(requires_grad) {</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        <span class="keywordflow">if</span> (std::distance(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>.begin(), <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>.end()) &lt; _size) {</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>            throw std::invalid_argument(<span class="stringliteral">&quot;Initializer list size is less than the tensor size.&quot;</span>);</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        }</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        <a class="code hl_class" href="classnz_1_1cu_strm_1_1_stream_manager.html">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;_data, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>        if (_requires_grad) {</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;_grad, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        }</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>            _grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        }</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <span class="keyword">auto</span> host_buf = <span class="keyword">new</span> value_type[_size];</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        <span class="keyword">auto</span> it = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>.begin();</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _size; ++i, ++it) {</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>            host_buf[i] = *it;</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        }</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_data, host_buf, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>                                                             cudaMemcpyHostToDevice);</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        <span class="keyword">delete</span>[] host_buf;</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>    }</div>
</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span> </div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>    <span class="comment">// Copy and Move constructors</span></div>
<div class="foldopen" id="foldopen00146" data-start="{" data-end="}">
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a6184f0270420ac054f7bd372bbed1406">  146</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad0dda0efff93778cab46fd5aa708b983">Tensor::Tensor</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; other) :</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        _size(other._size), _shape(other._shape), _requires_grad(other._requires_grad) {</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;_data, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_data, other._data, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>                                                             cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;_grad, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_grad, other._grad, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>                                                                 cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        }</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>            _grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        }</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>    }</div>
</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span> </div>
<div class="foldopen" id="foldopen00161" data-start="{" data-end="}">
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#adb57f91ae907875d78d804de85dbbc73">  161</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad0dda0efff93778cab46fd5aa708b983">Tensor::Tensor</a>(<a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp;&amp; other) <span class="keyword">noexcept</span>(<span class="keyword">false</span>):</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        _size(other._size), _shape(other._shape), _requires_grad(other._requires_grad) {</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        _data = other._data;</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>            _grad = other._grad;</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        }</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        other._data = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        other._grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        other._size = 0;</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        other._shape = {0, 0, 0, 0};</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>    }</div>
</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span> </div>
<div class="foldopen" id="foldopen00173" data-start="{" data-end="}">
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#acdb68bf53d38e5a93fdd0effa4c3059a">  173</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#acdb68bf53d38e5a93fdd0effa4c3059a">Tensor::operator=</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; other) {</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        <span class="keywordflow">if</span> (<span class="keyword">this</span> != &amp;other) {</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>            _size = other._size;</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>            _shape = other._shape;</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>            _requires_grad = other._requires_grad;</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(_data);</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;_data, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_data, other._data, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>                                                                 cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>            <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(_grad);</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;_grad, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_grad, other._grad, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>                                                                     cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>            }</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>        }</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>        <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>    }</div>
</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span> </div>
<div class="foldopen" id="foldopen00192" data-start="{" data-end="}">
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a26b24cc132d14e054b3c25923516d781">  192</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#acdb68bf53d38e5a93fdd0effa4c3059a">Tensor::operator=</a>(<a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp;&amp; other) <span class="keyword">noexcept</span>(<span class="keyword">false</span>) {</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>        <span class="keywordflow">if</span> (<span class="keyword">this</span> != &amp;other) {</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>            _size = other._size;</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>            _shape = other._shape;</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(_data);</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>            _data = other._data;</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>            other._data = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>            <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(_grad);</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>                _grad = other._grad;</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>                other._grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>            }</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>            other._size = 0;</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>            other._shape = {0, 0, 0, 0};</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>        }</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>        <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>    }</div>
</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span> </div>
<div class="foldopen" id="foldopen00210" data-start="{" data-end="}">
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a98a8b254d2b6c8b4893d7a286452a9b0">  210</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a98a8b254d2b6c8b4893d7a286452a9b0">Tensor::~Tensor</a>() noexcept(false) {</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        <span class="keywordflow">if</span> (_data != <span class="keyword">nullptr</span>) {</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a1084057ef6f5b2871c60702209bb4469">freeAsync</a>(_data);</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>            _data = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        }</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>            <span class="keywordflow">if</span> (_grad != <span class="keyword">nullptr</span>) {</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a1084057ef6f5b2871c60702209bb4469">freeAsync</a>(_grad);</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>                _grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>            }</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>        }</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>    }</div>
</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span> </div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>    <span class="comment">// Getter methods</span></div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a7cbc6dd248b882c95840835d0deaae1c">  224</a></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a7cbc6dd248b882c95840835d0deaae1c">Tensor::requiresGrad</a>() const noexcept { <span class="keywordflow">return</span> _requires_grad; }</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#aade7b0c42622279888d755f4f7989aac">  225</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#aade7b0c42622279888d755f4f7989aac">Tensor::shape</a>() const noexcept { <span class="keywordflow">return</span> _shape; }</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">  226</a></span>    Tensor::size_type <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">Tensor::size</a>() const noexcept { <span class="keywordflow">return</span> _size; }</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span> </div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>    <span class="comment">// Setter methods</span></div>
<div class="foldopen" id="foldopen00229" data-start="{" data-end="}">
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#abddb47a6dc305d289a1e4f91d01a5082">  229</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#abddb47a6dc305d289a1e4f91d01a5082">Tensor::setRequiresGrad</a>(<span class="keyword">const</span> <span class="keywordtype">bool</span> requires_grad) {</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>        <span class="keywordflow">if</span> (requires_grad &amp;&amp; _grad == <span class="keyword">nullptr</span>) {</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(<span class="keyword">reinterpret_cast&lt;</span>value_type**<span class="keyword">&gt;</span>(_grad),</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>                                                                 _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>        }</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>        <span class="keywordflow">if</span> (!requires_grad &amp;&amp; _grad != <span class="keyword">nullptr</span>) {</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(_grad);</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>            _grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>        }</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>        _requires_grad = requires_grad;</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>    }</div>
</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span> </div>
<div class="foldopen" id="foldopen00241" data-start="{" data-end="}">
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a2c4217ad3ebcdb4a1bcf2fd38151d007">  241</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#adf80894b8e06f260bb2695951e2f539e">Tensor::dataInject</a>(<span class="keyword">const</span> std::initializer_list&lt;value_type&gt;&amp; data, <span class="keyword">const</span> <span class="keywordtype">bool</span> grad)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>        <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#adf80894b8e06f260bb2695951e2f539e">dataInject</a>(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>.begin(), <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>.end(), <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad6107b98beb881d0209345185d5ad145">grad</a>);</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>    }</div>
</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span> </div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>    <span class="comment">// Operations</span></div>
<div class="foldopen" id="foldopen00246" data-start="{" data-end="}">
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a6fed8efad540a7621dd6640b2f2466d0">  246</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a6fed8efad540a7621dd6640b2f2466d0">Tensor::zeroGrad</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a71ad766cb2869d3dd6a3931966e81706">memset</a>(_grad, 0, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>        }</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>    }</div>
</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span> </div>
<div class="foldopen" id="foldopen00252" data-start="{" data-end="}">
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a2b2309d5428331f2e6f88037bb123c8f">  252</a></span>    std::ostream&amp; <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a2b2309d5428331f2e6f88037bb123c8f">Tensor::print</a>(std::ostream&amp; os)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>        <span class="keyword">const</span> std::ostream_iterator&lt;value_type&gt; output_iterator(os, <span class="stringliteral">&quot; &quot;</span>);</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>        <span class="keyword">auto</span>* <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a> = <span class="keyword">static_cast&lt;</span>value_type*<span class="keyword">&gt;</span>(malloc(_size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, _data, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>                                                             cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>);</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; n = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#acc472e84b4c44f649f34b6fbb0eeacf7">N</a>();</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; c = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ae1e87c4a462dd60e02821aa27ffc7e09">C</a>();</div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; h = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a7eb3acc882c48e775c418d97f709240f">H</a>();</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; w = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a65773c675476dfea3f06b30f21ebbedd">W</a>();</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> ni = 0; ni &lt; n; ++ni) {</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>            os &lt;&lt; <span class="stringliteral">&quot;n=&quot;</span> &lt;&lt; ni &lt;&lt; <span class="stringliteral">&quot; [\n&quot;</span>;</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> ci = 0; ci &lt; c; ++ci) {</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>                os &lt;&lt; <span class="stringliteral">&quot;  c=&quot;</span> &lt;&lt; ci &lt;&lt; <span class="stringliteral">&quot; [\n&quot;</span>;</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>                <span class="keywordflow">for</span> (<span class="keyword">auto</span> hi = 0; hi &lt; h; ++hi) {</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>                    <span class="keyword">const</span> <span class="keyword">auto</span> offset = ni * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(0) + ci * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(1) + hi * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(2);</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>                    <span class="keyword">const</span> <span class="keyword">auto</span>* begin = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a> + offset;</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>                    <span class="keyword">const</span> <span class="keyword">auto</span>* end = begin + w;</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>                    os &lt;&lt; <span class="stringliteral">&quot;    [&quot;</span>;</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>                    std::copy(begin, end, output_iterator);</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>                    os &lt;&lt; <span class="stringliteral">&quot;]\n&quot;</span>;</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>                }</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>                os &lt;&lt; <span class="stringliteral">&quot;  ]\n&quot;</span>;</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>            }</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>            os &lt;&lt; <span class="stringliteral">&quot;]\n\n&quot;</span>;</div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>        }</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>        free(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>);</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>        <span class="keywordflow">return</span> os;</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>    }</div>
</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span> </div>
<div class="foldopen" id="foldopen00282" data-start="{" data-end="}">
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#adf80894b8e06f260bb2695951e2f539e">  282</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#adf80894b8e06f260bb2695951e2f539e">Tensor::dataInject</a>(value_type* data, <span class="keyword">const</span> <span class="keywordtype">bool</span> grad)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad6107b98beb881d0209345185d5ad145">grad</a>) {</div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>            <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_grad, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>                                                                     cudaMemcpyHostToDevice);</div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>            }</div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>            <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>                <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">&quot;Tensor does not require gradients&quot;</span>);</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>            }</div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>        }</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_data, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span>                                                                 cudaMemcpyHostToDevice);</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span>        }</div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>    }</div>
</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span> </div>
<div class="foldopen" id="foldopen00298" data-start="{" data-end="}">
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a7a9f1d5fae2989181645e5f59f7666d8">  298</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a7a9f1d5fae2989181645e5f59f7666d8">Tensor::randomize</a>(<span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> <span class="keywordtype">long</span> seed)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a731986c2c4ecd056562eaddadef46df8">randomize</a>(_data, _size, seed, CURAND_RNG_PSEUDO_DEFAULT);</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>    }</div>
</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span> </div>
<div class="foldopen" id="foldopen00302" data-start="{" data-end="}">
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#afc4e6385b97cf7ceb8bb74748b73b681">  302</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#afc4e6385b97cf7ceb8bb74748b73b681">Tensor::clear</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a71ad766cb2869d3dd6a3931966e81706">memset</a>(_data, 0, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>    }</div>
</div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span> </div>
<div class="foldopen" id="foldopen00306" data-start="{" data-end="}">
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ad220de56b18c404611f07f2290cd7e9d">  306</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad220de56b18c404611f07f2290cd7e9d">Tensor::fill</a>(<span class="keyword">const</span> value_type value, <span class="keyword">const</span> <span class="keywordtype">bool</span> isGrad)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span>        <span class="keywordflow">if</span> (isGrad &amp;&amp; !_requires_grad) {</div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span>            <span class="keywordflow">throw</span> std::invalid_argument(</div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>                <span class="stringliteral">&quot;Gradient filling is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span>        }</div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>        <span class="keyword">const</span> dim3 block(512);</div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>        <span class="keyword">const</span> dim3 grid((_size + block.x - 1) / block.x);</div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#ad136c8a6560a5305984ce0a31bea71bf">krnl::Fill</a>(grid, block, isGrad ? _grad : _data, value, _size);</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>    }</div>
</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span> </div>
<div class="foldopen" id="foldopen00316" data-start="{" data-end="}">
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ae6144f6d7fa612d98538f17baf4ef574">  316</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ae6144f6d7fa612d98538f17baf4ef574">Tensor::fillMatrix</a>(<span class="keyword">const</span> value_type value, <span class="keyword">const</span> size_type batch, <span class="keyword">const</span> size_type channels,</div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>                            <span class="keyword">const</span> <span class="keywordtype">bool</span> isGrad) {</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span>        <span class="keywordflow">if</span> (batch &gt;= _shape[0] || channels &gt;= _shape[1]) {</div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid batch or channels&quot;</span>);</div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>        }</div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>        <span class="keywordflow">if</span> (isGrad &amp;&amp; !_requires_grad) {</div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>            <span class="keywordflow">throw</span> std::invalid_argument(</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>                <span class="stringliteral">&quot;Gradient filling is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>        }</div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span>        <span class="keyword">const</span> dim3 block(512);</div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno">  326</span>        <span class="keyword">const</span> dim3 grid((_shape[2] * _shape[3] + block.x - 1) / block.x);</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span>        <span class="keyword">const</span> <span class="keyword">auto</span> offset = batch * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(0) + channels * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(1);</div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno">  328</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#ad136c8a6560a5305984ce0a31bea71bf">krnl::Fill</a>(grid, block, (isGrad ? _grad : _data), value, _shape[2] * _shape[3], offset);</div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>    }</div>
</div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span> </div>
<div class="foldopen" id="foldopen00331" data-start="{" data-end="}">
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a36cd1679c45059de64deeca9152b0288">  331</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a36cd1679c45059de64deeca9152b0288">Tensor::operator+</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span>        <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a> result(_shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ab4f9f0cec97b8e579b62ccb37975de3c">Broadcast</a>(other._shape), _requires_grad || other._requires_grad);</div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span>        <a class="code hl_function" href="namespacenz_1_1data.html#a8cf4ac2437dd67698684169bebb225d4">tensorMatrixAdd</a>(result, *<span class="keyword">this</span>, other);</div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span>    }</div>
</div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span> </div>
<div class="foldopen" id="foldopen00337" data-start="{" data-end="}">
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a25cc6634977413df0b67d6e7365448a2">  337</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad66d0c0f5d9ecb375e1006bc0aecf404">Tensor::operator-</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span>        <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a> result(_shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ab4f9f0cec97b8e579b62ccb37975de3c">Broadcast</a>(other._shape), _requires_grad || other._requires_grad);</div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span>        <a class="code hl_function" href="namespacenz_1_1data.html#a7503b6894e8052ed54eb169550d135c0">tensorMatrixSub</a>(result, *<span class="keyword">this</span>, other);</div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span>    }</div>
</div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno">  342</span> </div>
<div class="foldopen" id="foldopen00343" data-start="{" data-end="}">
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#aaa22ac6f3de75ee92a4307320eda7e87">  343</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#aaa22ac6f3de75ee92a4307320eda7e87">Tensor::operator*</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno">  344</span>        <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a> result({</div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span>                          std::max(_shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#acc472e84b4c44f649f34b6fbb0eeacf7">N</a>(), other._shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#acc472e84b4c44f649f34b6fbb0eeacf7">N</a>()), std::max(_shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ae1e87c4a462dd60e02821aa27ffc7e09">C</a>(), other._shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ae1e87c4a462dd60e02821aa27ffc7e09">C</a>()), _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a7eb3acc882c48e775c418d97f709240f">H</a>(),</div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span>                          other._shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a65773c675476dfea3f06b30f21ebbedd">W</a>()</div>
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno">  347</span>                      }, _requires_grad || other._requires_grad);</div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span>        <a class="code hl_function" href="namespacenz_1_1data.html#a5a166a472b887c45fde9e5815f072234">tensorGeneralMatrixMul</a>(result, *<span class="keyword">this</span>, other);</div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span>    }</div>
</div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno">  351</span> </div>
<div class="foldopen" id="foldopen00352" data-start="{" data-end="}">
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ad6ac34675276afe1fb2ee2f5d16af538">  352</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad6ac34675276afe1fb2ee2f5d16af538">Tensor::operator/</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span>        <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a> result(_shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ab4f9f0cec97b8e579b62ccb37975de3c">Broadcast</a>(other._shape), _requires_grad || other._requires_grad);</div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno">  354</span>        <a class="code hl_function" href="namespacenz_1_1data.html#a1da5cd018533919ed5a750b14c7d6d71">tensorElementwiseDivide</a>(result, *<span class="keyword">this</span>, other);</div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno">  355</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno">  356</span>    }</div>
</div>
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno">  357</span> </div>
<div class="foldopen" id="foldopen00358" data-start="{" data-end="}">
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a877f9f2704e39100142d81d289ddc3f2">  358</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a877f9f2704e39100142d81d289ddc3f2">Tensor::reshape</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">shape_type</a>&amp; shape) {</div>
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno">  359</span>        <span class="keyword">const</span> size_type <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#aade7b0c42622279888d755f4f7989aac">shape</a>.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a073622bb031999163987ccf77f8edfb2">size</a>();</div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno">  360</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> != _size) {</div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno">  361</span>            WARN(<span class="stringliteral">&quot;Reshaping to a different size will cause data loss&quot;</span>);</div>
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno">  362</span>        }</div>
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno">  363</span>        value_type* temp;</div>
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno">  364</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;temp, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno">  365</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a71ad766cb2869d3dd6a3931966e81706">memset</a>(temp, 0, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno">  366</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(temp, _data,</div>
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno">  367</span>                                                             (<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> &lt; _size ? <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> : _size) * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno">  368</span>                                                             cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno">  369</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(_data);</div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno">  370</span>        _data = temp;</div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno">  371</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno">  372</span>            value_type* tempGrad;</div>
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno">  373</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;tempGrad, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno">  374</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a71ad766cb2869d3dd6a3931966e81706">memset</a>(tempGrad, 0, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno">  375</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(tempGrad, _grad,</div>
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno">  376</span>                                                                 (<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> &lt; _size ? <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> : _size) * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno">  377</span>                                                                 cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno">  378</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(_grad);</div>
<div class="line"><a id="l00379" name="l00379"></a><span class="lineno">  379</span>            _grad = tempGrad;</div>
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno">  380</span>        }</div>
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno">  381</span>        _shape = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#aade7b0c42622279888d755f4f7989aac">shape</a>;</div>
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno">  382</span>        _size = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a>;</div>
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno">  383</span>    }</div>
</div>
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno">  384</span> </div>
<div class="foldopen" id="foldopen00385" data-start="{" data-end="}">
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a45e6f84ae74111ced9a96bdf204b2294">  385</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a45e6f84ae74111ced9a96bdf204b2294">Tensor::transpose</a>() {</div>
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno">  386</span>        <span class="keyword">const</span> dim3 block(TILE_SIZE, TILE_SIZE);</div>
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno">  387</span>        <span class="keyword">const</span> dim3 grid((_shape[2] + block.x - 1) / block.x, (_shape[3] + block.y - 1) / block.y);</div>
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno">  388</span>        value_type* temp;</div>
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno">  389</span>        std::vector&lt;size_t&gt; offset;</div>
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno">  390</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;float&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;temp, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno">  391</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _shape[0]; i += 1) {</div>
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno">  392</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; _shape[1]; j += 1) {</div>
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno">  393</span>                offset.push_back(i * _shape.getStride(0) + j * _shape.getStride(1));</div>
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno">  394</span>            }</div>
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno">  395</span>        }</div>
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno">  396</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#afe3f38f788c735b7eb718443eb0fd094">krnl::Transpose</a>(grid, block, _data, temp, _shape[2], _shape[3], offset);</div>
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno">  397</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a1084057ef6f5b2871c60702209bb4469">freeAsync</a>(_data);</div>
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno">  398</span>        _data = temp;</div>
<div class="line"><a id="l00399" name="l00399"></a><span class="lineno">  399</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00400" name="l00400"></a><span class="lineno">  400</span>            value_type* tempGrad;</div>
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno">  401</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;float&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;tempGrad, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00402" name="l00402"></a><span class="lineno">  402</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#afe3f38f788c735b7eb718443eb0fd094">krnl::Transpose</a>(grid, block, _grad, tempGrad, _shape[2], _shape[3], offset);</div>
<div class="line"><a id="l00403" name="l00403"></a><span class="lineno">  403</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a1084057ef6f5b2871c60702209bb4469">freeAsync</a>(_grad);</div>
<div class="line"><a id="l00404" name="l00404"></a><span class="lineno">  404</span>            _grad = tempGrad;</div>
<div class="line"><a id="l00405" name="l00405"></a><span class="lineno">  405</span>        }</div>
<div class="line"><a id="l00406" name="l00406"></a><span class="lineno">  406</span>        std::swap(_shape[2], _shape[3]);</div>
<div class="line"><a id="l00407" name="l00407"></a><span class="lineno">  407</span>        _shape.updateStride();</div>
<div class="line"><a id="l00408" name="l00408"></a><span class="lineno">  408</span>    }</div>
</div>
<div class="line"><a id="l00409" name="l00409"></a><span class="lineno">  409</span> </div>
<div class="foldopen" id="foldopen00410" data-start="{" data-end="}">
<div class="line"><a id="l00410" name="l00410"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a2f9be06ac6766a5fa6de3548c722ef43">  410</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a2f9be06ac6766a5fa6de3548c722ef43">Tensor::setData</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">shape_type</a>&amp; position, <span class="keyword">const</span> value_type value, <span class="keyword">const</span> <span class="keywordtype">bool</span> isGrad)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00411" name="l00411"></a><span class="lineno">  411</span>        <span class="keywordflow">if</span> (position[0] &gt;= _shape[0] || position[1] &gt;= _shape[1] || position[2] &gt;= _shape[2] || position[3] &gt;= _shape[</div>
<div class="line"><a id="l00412" name="l00412"></a><span class="lineno">  412</span>            3]) {</div>
<div class="line"><a id="l00413" name="l00413"></a><span class="lineno">  413</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid position&quot;</span>);</div>
<div class="line"><a id="l00414" name="l00414"></a><span class="lineno">  414</span>        }</div>
<div class="line"><a id="l00415" name="l00415"></a><span class="lineno">  415</span>        <span class="keywordflow">if</span> (isGrad &amp;&amp; !_requires_grad) {</div>
<div class="line"><a id="l00416" name="l00416"></a><span class="lineno">  416</span>            <span class="keywordflow">throw</span> std::invalid_argument(</div>
<div class="line"><a id="l00417" name="l00417"></a><span class="lineno">  417</span>                <span class="stringliteral">&quot;Gradient setting is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00418" name="l00418"></a><span class="lineno">  418</span>        }</div>
<div class="line"><a id="l00419" name="l00419"></a><span class="lineno">  419</span>        <span class="keyword">auto</span>* <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a> = <span class="keyword">static_cast&lt;</span>value_type*<span class="keyword">&gt;</span>(malloc(_size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00420" name="l00420"></a><span class="lineno">  420</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, isGrad ? _grad : _data, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00421" name="l00421"></a><span class="lineno">  421</span>                                                             cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00422" name="l00422"></a><span class="lineno">  422</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>);</div>
<div class="line"><a id="l00423" name="l00423"></a><span class="lineno">  423</span>        <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>[position[0] * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(0) + position[1] * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(1) + position[2] * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(2) +</div>
<div class="line"><a id="l00424" name="l00424"></a><span class="lineno">  424</span>            position[3] * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(3)] = value;</div>
<div class="line"><a id="l00425" name="l00425"></a><span class="lineno">  425</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(isGrad ? _grad : _data, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00426" name="l00426"></a><span class="lineno">  426</span>                                                             cudaMemcpyHostToDevice);</div>
<div class="line"><a id="l00427" name="l00427"></a><span class="lineno">  427</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(isGrad ? _grad : _data);</div>
<div class="line"><a id="l00428" name="l00428"></a><span class="lineno">  428</span>        free(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>);</div>
<div class="line"><a id="l00429" name="l00429"></a><span class="lineno">  429</span>    }</div>
</div>
<div class="line"><a id="l00430" name="l00430"></a><span class="lineno">  430</span> </div>
<div class="line"><a id="l00431" name="l00431"></a><span class="lineno">  431</span> </div>
<div class="foldopen" id="foldopen00432" data-start="{" data-end="}">
<div class="line"><a id="l00432" name="l00432"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">  432</a></span>    Tensor::value_type* <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">Tensor::data</a>() const noexcept {</div>
<div class="line"><a id="l00433" name="l00433"></a><span class="lineno">  433</span>        <span class="keywordflow">return</span> _data;</div>
<div class="line"><a id="l00434" name="l00434"></a><span class="lineno">  434</span>    }</div>
</div>
<div class="line"><a id="l00435" name="l00435"></a><span class="lineno">  435</span> </div>
<div class="foldopen" id="foldopen00436" data-start="{" data-end="}">
<div class="line"><a id="l00436" name="l00436"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a615af61999990e2edebacf5afbad0e57">  436</a></span>    std::vector&lt;Tensor::value_type&gt; <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a615af61999990e2edebacf5afbad0e57">Tensor::hostData</a>() const noexcept {</div>
<div class="line"><a id="l00437" name="l00437"></a><span class="lineno">  437</span>        <span class="keyword">auto</span> temp = <span class="keyword">new</span> value_type[_size];</div>
<div class="line"><a id="l00438" name="l00438"></a><span class="lineno">  438</span>        <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a7aab89d371ff013c5c021a191bd7348e">syncData</a>();</div>
<div class="line"><a id="l00439" name="l00439"></a><span class="lineno">  439</span>        cudaMemcpy(temp, _data, _size * <span class="keyword">sizeof</span>(value_type), cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00440" name="l00440"></a><span class="lineno">  440</span>        std::vector result(temp, temp + _size);</div>
<div class="line"><a id="l00441" name="l00441"></a><span class="lineno">  441</span>        <span class="keyword">delete</span>[] temp;</div>
<div class="line"><a id="l00442" name="l00442"></a><span class="lineno">  442</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00443" name="l00443"></a><span class="lineno">  443</span>    }</div>
</div>
<div class="line"><a id="l00444" name="l00444"></a><span class="lineno">  444</span> </div>
<div class="foldopen" id="foldopen00445" data-start="{" data-end="}">
<div class="line"><a id="l00445" name="l00445"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ad6107b98beb881d0209345185d5ad145">  445</a></span>    Tensor::value_type* <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad6107b98beb881d0209345185d5ad145">Tensor::grad</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00446" name="l00446"></a><span class="lineno">  446</span>        <span class="keywordflow">if</span> (!_requires_grad) {</div>
<div class="line"><a id="l00447" name="l00447"></a><span class="lineno">  447</span>            <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">&quot;Tensor does not require gradients&quot;</span>);</div>
<div class="line"><a id="l00448" name="l00448"></a><span class="lineno">  448</span>        }</div>
<div class="line"><a id="l00449" name="l00449"></a><span class="lineno">  449</span>        <span class="keywordflow">return</span> _grad;</div>
<div class="line"><a id="l00450" name="l00450"></a><span class="lineno">  450</span>    }</div>
</div>
<div class="line"><a id="l00451" name="l00451"></a><span class="lineno">  451</span> </div>
<div class="foldopen" id="foldopen00452" data-start="{" data-end="}">
<div class="line"><a id="l00452" name="l00452"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a7fd4badf84f9c5398e08b23a9826dfbc">  452</a></span>    std::vector&lt;Tensor::value_type&gt; <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a7fd4badf84f9c5398e08b23a9826dfbc">Tensor::hostGrad</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00453" name="l00453"></a><span class="lineno">  453</span>        <span class="keywordflow">if</span> (!_requires_grad) {</div>
<div class="line"><a id="l00454" name="l00454"></a><span class="lineno">  454</span>            <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">&quot;Tensor does not require gradients&quot;</span>);</div>
<div class="line"><a id="l00455" name="l00455"></a><span class="lineno">  455</span>        }</div>
<div class="line"><a id="l00456" name="l00456"></a><span class="lineno">  456</span>        <span class="keyword">auto</span> temp = <span class="keyword">new</span> value_type[_size];</div>
<div class="line"><a id="l00457" name="l00457"></a><span class="lineno">  457</span>        <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#af28425ddc9bee1f75fd923a0de68c37b">syncGrad</a>();</div>
<div class="line"><a id="l00458" name="l00458"></a><span class="lineno">  458</span>        cudaMemcpy(temp, _grad, _size * <span class="keyword">sizeof</span>(value_type), cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00459" name="l00459"></a><span class="lineno">  459</span>        std::vector result(temp, temp + _size);</div>
<div class="line"><a id="l00460" name="l00460"></a><span class="lineno">  460</span>        <span class="keyword">delete</span>[] temp;</div>
<div class="line"><a id="l00461" name="l00461"></a><span class="lineno">  461</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00462" name="l00462"></a><span class="lineno">  462</span>    }</div>
</div>
<div class="line"><a id="l00463" name="l00463"></a><span class="lineno">  463</span> </div>
<div class="foldopen" id="foldopen00464" data-start="{" data-end="}">
<div class="line"><a id="l00464" name="l00464"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a4b02ed4d2afec1ce75931201af181e14">  464</a></span>    std::ostream&amp; <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a4b02ed4d2afec1ce75931201af181e14">Tensor::printGrad</a>(std::ostream&amp; os)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00465" name="l00465"></a><span class="lineno">  465</span>        <span class="keywordflow">if</span> (!_requires_grad) {</div>
<div class="line"><a id="l00466" name="l00466"></a><span class="lineno">  466</span>            <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">&quot;Tensor does not require gradients&quot;</span>);</div>
<div class="line"><a id="l00467" name="l00467"></a><span class="lineno">  467</span>        }</div>
<div class="line"><a id="l00468" name="l00468"></a><span class="lineno">  468</span>        <span class="keyword">auto</span>* <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a> = <span class="keyword">static_cast&lt;</span>value_type*<span class="keyword">&gt;</span>(malloc(_size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00469" name="l00469"></a><span class="lineno">  469</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, _grad, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00470" name="l00470"></a><span class="lineno">  470</span>                                                             cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00471" name="l00471"></a><span class="lineno">  471</span>        <span class="keyword">const</span> std::ostream_iterator&lt;value_type&gt; output_iterator(os, <span class="stringliteral">&quot; &quot;</span>);</div>
<div class="line"><a id="l00472" name="l00472"></a><span class="lineno">  472</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>);</div>
<div class="line"><a id="l00473" name="l00473"></a><span class="lineno">  473</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; n = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#acc472e84b4c44f649f34b6fbb0eeacf7">N</a>();</div>
<div class="line"><a id="l00474" name="l00474"></a><span class="lineno">  474</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; c = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ae1e87c4a462dd60e02821aa27ffc7e09">C</a>();</div>
<div class="line"><a id="l00475" name="l00475"></a><span class="lineno">  475</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; h = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a7eb3acc882c48e775c418d97f709240f">H</a>();</div>
<div class="line"><a id="l00476" name="l00476"></a><span class="lineno">  476</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; w = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a65773c675476dfea3f06b30f21ebbedd">W</a>();</div>
<div class="line"><a id="l00477" name="l00477"></a><span class="lineno">  477</span> </div>
<div class="line"><a id="l00478" name="l00478"></a><span class="lineno">  478</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> ni = 0; ni &lt; n; ++ni) {</div>
<div class="line"><a id="l00479" name="l00479"></a><span class="lineno">  479</span>            os &lt;&lt; <span class="stringliteral">&quot;n=&quot;</span> &lt;&lt; ni &lt;&lt; <span class="stringliteral">&quot; [\n&quot;</span>;</div>
<div class="line"><a id="l00480" name="l00480"></a><span class="lineno">  480</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> ci = 0; ci &lt; c; ++ci) {</div>
<div class="line"><a id="l00481" name="l00481"></a><span class="lineno">  481</span>                os &lt;&lt; <span class="stringliteral">&quot;  c=&quot;</span> &lt;&lt; ci &lt;&lt; <span class="stringliteral">&quot; [\n&quot;</span>;</div>
<div class="line"><a id="l00482" name="l00482"></a><span class="lineno">  482</span>                <span class="keywordflow">for</span> (<span class="keyword">auto</span> hi = 0; hi &lt; h; ++hi) {</div>
<div class="line"><a id="l00483" name="l00483"></a><span class="lineno">  483</span>                    <span class="keyword">const</span> <span class="keyword">auto</span> offset = ni * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(0) + ci * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(1) + hi * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(2);</div>
<div class="line"><a id="l00484" name="l00484"></a><span class="lineno">  484</span>                    <span class="keyword">const</span> <span class="keyword">auto</span>* begin = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a> + offset;</div>
<div class="line"><a id="l00485" name="l00485"></a><span class="lineno">  485</span>                    <span class="keyword">const</span> <span class="keyword">auto</span>* end = begin + w;</div>
<div class="line"><a id="l00486" name="l00486"></a><span class="lineno">  486</span>                    os &lt;&lt; <span class="stringliteral">&quot;    [&quot;</span>;</div>
<div class="line"><a id="l00487" name="l00487"></a><span class="lineno">  487</span>                    std::copy(begin, end, output_iterator);</div>
<div class="line"><a id="l00488" name="l00488"></a><span class="lineno">  488</span>                    os &lt;&lt; <span class="stringliteral">&quot;]\n&quot;</span>;</div>
<div class="line"><a id="l00489" name="l00489"></a><span class="lineno">  489</span>                }</div>
<div class="line"><a id="l00490" name="l00490"></a><span class="lineno">  490</span>                os &lt;&lt; <span class="stringliteral">&quot;  ]\n&quot;</span>;</div>
<div class="line"><a id="l00491" name="l00491"></a><span class="lineno">  491</span>            }</div>
<div class="line"><a id="l00492" name="l00492"></a><span class="lineno">  492</span>            os &lt;&lt; <span class="stringliteral">&quot;]\n\n&quot;</span>;</div>
<div class="line"><a id="l00493" name="l00493"></a><span class="lineno">  493</span>        }</div>
<div class="line"><a id="l00494" name="l00494"></a><span class="lineno">  494</span>        free(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>);</div>
<div class="line"><a id="l00495" name="l00495"></a><span class="lineno">  495</span>        <span class="keywordflow">return</span> os;</div>
<div class="line"><a id="l00496" name="l00496"></a><span class="lineno">  496</span>    }</div>
</div>
<div class="line"><a id="l00497" name="l00497"></a><span class="lineno">  497</span> </div>
<div class="foldopen" id="foldopen00498" data-start="{" data-end="}">
<div class="line"><a id="l00498" name="l00498"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ad66d0c0f5d9ecb375e1006bc0aecf404">  498</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#ad66d0c0f5d9ecb375e1006bc0aecf404">Tensor::operator-</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00499" name="l00499"></a><span class="lineno">  499</span>        <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a> result(_shape, _requires_grad);</div>
<div class="line"><a id="l00500" name="l00500"></a><span class="lineno">  500</span>        <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00501" name="l00501"></a><span class="lineno">  501</span>        <span class="keyword">const</span> dim3 grid((_size + block.x - 1) / block.x);</div>
<div class="line"><a id="l00502" name="l00502"></a><span class="lineno">  502</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#af7069a420e81babb49b1bc009333d053">krnl::Negation</a>(grid, block, result._data, _data, _size);</div>
<div class="line"><a id="l00503" name="l00503"></a><span class="lineno">  503</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00504" name="l00504"></a><span class="lineno">  504</span>    }</div>
</div>
<div class="line"><a id="l00505" name="l00505"></a><span class="lineno">  505</span> </div>
<div class="foldopen" id="foldopen00506" data-start="{" data-end="}">
<div class="line"><a id="l00506" name="l00506"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a92c7313608326bb4123d6f08341a6d80">  506</a></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a92c7313608326bb4123d6f08341a6d80">Tensor::operator==</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00507" name="l00507"></a><span class="lineno">  507</span>        <span class="keywordflow">if</span> (_requires_grad != other._requires_grad) {</div>
<div class="line"><a id="l00508" name="l00508"></a><span class="lineno">  508</span>            <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a id="l00509" name="l00509"></a><span class="lineno">  509</span>        }</div>
<div class="line"><a id="l00510" name="l00510"></a><span class="lineno">  510</span>        <span class="keywordflow">if</span> (_shape != other._shape) {</div>
<div class="line"><a id="l00511" name="l00511"></a><span class="lineno">  511</span>            <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a id="l00512" name="l00512"></a><span class="lineno">  512</span>        }</div>
<div class="line"><a id="l00513" name="l00513"></a><span class="lineno">  513</span>        <span class="keyword">constexpr</span> value_type abs_epsilon = 1e-6f;</div>
<div class="line"><a id="l00514" name="l00514"></a><span class="lineno">  514</span>        <span class="keyword">constexpr</span> value_type rel_epsilon = 1e-5f;</div>
<div class="line"><a id="l00515" name="l00515"></a><span class="lineno">  515</span>        this-&gt;<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a0c150b841f02921eb7826a6e03d0267e">sync</a>();</div>
<div class="line"><a id="l00516" name="l00516"></a><span class="lineno">  516</span>        other.<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a0c150b841f02921eb7826a6e03d0267e">sync</a>();</div>
<div class="line"><a id="l00517" name="l00517"></a><span class="lineno">  517</span>        <span class="keyword">auto</span>* temp = <span class="keyword">new</span> value_type[_size];</div>
<div class="line"><a id="l00518" name="l00518"></a><span class="lineno">  518</span>        <span class="keyword">auto</span>* temp_other = <span class="keyword">new</span> value_type[_size];</div>
<div class="line"><a id="l00519" name="l00519"></a><span class="lineno">  519</span>        cudaMemcpy(temp, _data, _size * <span class="keyword">sizeof</span>(value_type), cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00520" name="l00520"></a><span class="lineno">  520</span>        cudaMemcpy(temp_other, other._data, _size * <span class="keyword">sizeof</span>(value_type), cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00521" name="l00521"></a><span class="lineno">  521</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _size; i++) {</div>
<div class="line"><a id="l00522" name="l00522"></a><span class="lineno">  522</span>            <span class="keyword">const</span> <span class="keyword">auto</span> a = temp[i];</div>
<div class="line"><a id="l00523" name="l00523"></a><span class="lineno">  523</span>            <span class="keyword">const</span> <span class="keyword">auto</span> b = temp_other[i];</div>
<div class="line"><a id="l00524" name="l00524"></a><span class="lineno">  524</span>            <span class="keyword">const</span> <span class="keyword">auto</span> diff = std::abs(a - b);</div>
<div class="line"><a id="l00525" name="l00525"></a><span class="lineno">  525</span>            <span class="keywordflow">if</span> (diff &gt; std::max(rel_epsilon * std::max(std::abs(a), std::abs(b)), abs_epsilon)) {</div>
<div class="line"><a id="l00526" name="l00526"></a><span class="lineno">  526</span>                <span class="keyword">delete</span>[] temp;</div>
<div class="line"><a id="l00527" name="l00527"></a><span class="lineno">  527</span>                <span class="keyword">delete</span>[] temp_other;</div>
<div class="line"><a id="l00528" name="l00528"></a><span class="lineno">  528</span>                <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a id="l00529" name="l00529"></a><span class="lineno">  529</span>            }</div>
<div class="line"><a id="l00530" name="l00530"></a><span class="lineno">  530</span>        }</div>
<div class="line"><a id="l00531" name="l00531"></a><span class="lineno">  531</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00532" name="l00532"></a><span class="lineno">  532</span>            cudaMemcpy(temp, _grad, _size * <span class="keyword">sizeof</span>(value_type), cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00533" name="l00533"></a><span class="lineno">  533</span>            cudaMemcpy(temp_other, other._grad, _size * <span class="keyword">sizeof</span>(value_type), cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00534" name="l00534"></a><span class="lineno">  534</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _size; i++) {</div>
<div class="line"><a id="l00535" name="l00535"></a><span class="lineno">  535</span>                <span class="keyword">const</span> <span class="keyword">auto</span> a = temp[i];</div>
<div class="line"><a id="l00536" name="l00536"></a><span class="lineno">  536</span>                <span class="keyword">const</span> <span class="keyword">auto</span> b = temp_other[i];</div>
<div class="line"><a id="l00537" name="l00537"></a><span class="lineno">  537</span>                <span class="keyword">const</span> <span class="keyword">auto</span> diff = std::abs(a - b);</div>
<div class="line"><a id="l00538" name="l00538"></a><span class="lineno">  538</span>                <span class="keywordflow">if</span> (diff &gt; std::max(rel_epsilon * std::max(std::abs(a), std::abs(b)), abs_epsilon)) {</div>
<div class="line"><a id="l00539" name="l00539"></a><span class="lineno">  539</span>                    <span class="keyword">delete</span>[] temp;</div>
<div class="line"><a id="l00540" name="l00540"></a><span class="lineno">  540</span>                    <span class="keyword">delete</span>[] temp_other;</div>
<div class="line"><a id="l00541" name="l00541"></a><span class="lineno">  541</span>                    <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a id="l00542" name="l00542"></a><span class="lineno">  542</span>                }</div>
<div class="line"><a id="l00543" name="l00543"></a><span class="lineno">  543</span>            }</div>
<div class="line"><a id="l00544" name="l00544"></a><span class="lineno">  544</span>        }</div>
<div class="line"><a id="l00545" name="l00545"></a><span class="lineno">  545</span>        <span class="keyword">delete</span>[] temp;</div>
<div class="line"><a id="l00546" name="l00546"></a><span class="lineno">  546</span>        <span class="keyword">delete</span>[] temp_other;</div>
<div class="line"><a id="l00547" name="l00547"></a><span class="lineno">  547</span>        <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a id="l00548" name="l00548"></a><span class="lineno">  548</span>    }</div>
</div>
<div class="line"><a id="l00549" name="l00549"></a><span class="lineno">  549</span> </div>
<div class="foldopen" id="foldopen00550" data-start="{" data-end="}">
<div class="line"><a id="l00550" name="l00550"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#aae7b7714f78f4d366e66f1664d37d36a">  550</a></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#aae7b7714f78f4d366e66f1664d37d36a">Tensor::operator!=</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00551" name="l00551"></a><span class="lineno">  551</span>        <span class="keywordflow">return</span> !(*<span class="keyword">this</span> == other);</div>
<div class="line"><a id="l00552" name="l00552"></a><span class="lineno">  552</span>    }</div>
</div>
<div class="line"><a id="l00553" name="l00553"></a><span class="lineno">  553</span> </div>
<div class="foldopen" id="foldopen00554" data-start="{" data-end="}">
<div class="line"><a id="l00554" name="l00554"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a178a2240cd5d441be508490b2613fc55">  554</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a178a2240cd5d441be508490b2613fc55">Tensor::recip</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00555" name="l00555"></a><span class="lineno">  555</span>        value_type* <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>;</div>
<div class="line"><a id="l00556" name="l00556"></a><span class="lineno">  556</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00557" name="l00557"></a><span class="lineno">  557</span>        <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00558" name="l00558"></a><span class="lineno">  558</span>        <span class="keyword">const</span> dim3 grid((_size + block.x - 1) / block.x);</div>
<div class="line"><a id="l00559" name="l00559"></a><span class="lineno">  559</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#adc047e65307dbc711235f637227b7d10">krnl::Recip</a>(grid, block, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, _data, _size);</div>
<div class="line"><a id="l00560" name="l00560"></a><span class="lineno">  560</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_data, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00561" name="l00561"></a><span class="lineno">  561</span>                                                             cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00562" name="l00562"></a><span class="lineno">  562</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">data</a>);</div>
<div class="line"><a id="l00563" name="l00563"></a><span class="lineno">  563</span>    }</div>
</div>
<div class="line"><a id="l00564" name="l00564"></a><span class="lineno">  564</span> </div>
<div class="foldopen" id="foldopen00565" data-start="{" data-end="}">
<div class="line"><a id="l00565" name="l00565"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a4a657091dfa6a490d873ab8e95d9bb9e">  565</a></span>    Tensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a4a657091dfa6a490d873ab8e95d9bb9e">Tensor::sum</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00566" name="l00566"></a><span class="lineno">  566</span>        <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00567" name="l00567"></a><span class="lineno">  567</span>        <span class="keyword">const</span> dim3 grid((_size + block.x - 1) / block.x);</div>
<div class="line"><a id="l00568" name="l00568"></a><span class="lineno">  568</span>        value_type* dData;</div>
<div class="line"><a id="l00569" name="l00569"></a><span class="lineno">  569</span>        <span class="keyword">auto</span>* hData = <span class="keyword">new</span> value_type[grid.x];</div>
<div class="line"><a id="l00570" name="l00570"></a><span class="lineno">  570</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;dData, grid.x * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00571" name="l00571"></a><span class="lineno">  571</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#a1ae846a65c2f5b83cd1b9fc61b877854">krnl::Summation</a>(grid, block, block.x / WARP_SIZE * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), dData, _data, _size);</div>
<div class="line"><a id="l00572" name="l00572"></a><span class="lineno">  572</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(hData, dData, grid.x * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00573" name="l00573"></a><span class="lineno">  573</span>                                                             cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00574" name="l00574"></a><span class="lineno">  574</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(hData);</div>
<div class="line"><a id="l00575" name="l00575"></a><span class="lineno">  575</span>        value_type result = 0;</div>
<div class="line"><a id="l00576" name="l00576"></a><span class="lineno">  576</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; grid.x; ++i) {</div>
<div class="line"><a id="l00577" name="l00577"></a><span class="lineno">  577</span>            result += hData[i];</div>
<div class="line"><a id="l00578" name="l00578"></a><span class="lineno">  578</span>        }</div>
<div class="line"><a id="l00579" name="l00579"></a><span class="lineno">  579</span>        <span class="keyword">delete</span>[] hData;</div>
<div class="line"><a id="l00580" name="l00580"></a><span class="lineno">  580</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(dData);</div>
<div class="line"><a id="l00581" name="l00581"></a><span class="lineno">  581</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00582" name="l00582"></a><span class="lineno">  582</span>    }</div>
</div>
<div class="line"><a id="l00583" name="l00583"></a><span class="lineno">  583</span> </div>
<div class="foldopen" id="foldopen00584" data-start="{" data-end="}">
<div class="line"><a id="l00584" name="l00584"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a74aa515ba6b83aa1d05a7bb001b297b3">  584</a></span>    Tensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a4a657091dfa6a490d873ab8e95d9bb9e">Tensor::sum</a>(<span class="keyword">const</span> size_type batch, <span class="keyword">const</span> size_type channel)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00585" name="l00585"></a><span class="lineno">  585</span>        <span class="keywordflow">if</span> (batch &gt;= _shape[0] || channel &gt;= _shape[1]) {</div>
<div class="line"><a id="l00586" name="l00586"></a><span class="lineno">  586</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid position&quot;</span>);</div>
<div class="line"><a id="l00587" name="l00587"></a><span class="lineno">  587</span>        }</div>
<div class="line"><a id="l00588" name="l00588"></a><span class="lineno">  588</span>        <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> = _shape[2] * _shape[3];</div>
<div class="line"><a id="l00589" name="l00589"></a><span class="lineno">  589</span>        <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00590" name="l00590"></a><span class="lineno">  590</span>        <span class="keyword">const</span> dim3 grid((<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> + block.x - 1) / block.x);</div>
<div class="line"><a id="l00591" name="l00591"></a><span class="lineno">  591</span>        value_type* dData;</div>
<div class="line"><a id="l00592" name="l00592"></a><span class="lineno">  592</span>        <span class="keyword">auto</span>* hData = <span class="keyword">new</span> value_type[grid.x];</div>
<div class="line"><a id="l00593" name="l00593"></a><span class="lineno">  593</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;dData, grid.x * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00594" name="l00594"></a><span class="lineno">  594</span>        <span class="keyword">const</span> <span class="keyword">auto</span> offset = batch * _shape.getStride(0) + channel * _shape.getStride(1);</div>
<div class="line"><a id="l00595" name="l00595"></a><span class="lineno">  595</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#a1ae846a65c2f5b83cd1b9fc61b877854">krnl::Summation</a>(grid, block, block.x / WARP_SIZE * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), dData, _data, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a>, offset);</div>
<div class="line"><a id="l00596" name="l00596"></a><span class="lineno">  596</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(hData, dData, grid.x * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00597" name="l00597"></a><span class="lineno">  597</span>                                                             cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00598" name="l00598"></a><span class="lineno">  598</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(hData);</div>
<div class="line"><a id="l00599" name="l00599"></a><span class="lineno">  599</span>        value_type result = 0;</div>
<div class="line"><a id="l00600" name="l00600"></a><span class="lineno">  600</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; grid.x; ++i) {</div>
<div class="line"><a id="l00601" name="l00601"></a><span class="lineno">  601</span>            result += hData[i];</div>
<div class="line"><a id="l00602" name="l00602"></a><span class="lineno">  602</span>        }</div>
<div class="line"><a id="l00603" name="l00603"></a><span class="lineno">  603</span>        <span class="keyword">delete</span>[] hData;</div>
<div class="line"><a id="l00604" name="l00604"></a><span class="lineno">  604</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(dData);</div>
<div class="line"><a id="l00605" name="l00605"></a><span class="lineno">  605</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00606" name="l00606"></a><span class="lineno">  606</span>    }</div>
</div>
<div class="line"><a id="l00607" name="l00607"></a><span class="lineno">  607</span> </div>
<div class="foldopen" id="foldopen00608" data-start="{" data-end="}">
<div class="line"><a id="l00608" name="l00608"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a9131832f57339c0de2e7fb7955940a55">  608</a></span>    Tensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a9131832f57339c0de2e7fb7955940a55">Tensor::max</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00609" name="l00609"></a><span class="lineno">  609</span>        <span class="keyword">auto</span> hData = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a615af61999990e2edebacf5afbad0e57">hostData</a>();</div>
<div class="line"><a id="l00610" name="l00610"></a><span class="lineno">  610</span>        value_type result = std::numeric_limits&lt;value_type&gt;::min();</div>
<div class="line"><a id="l00611" name="l00611"></a><span class="lineno">  611</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _size; ++i) {</div>
<div class="line"><a id="l00612" name="l00612"></a><span class="lineno">  612</span>            <span class="keywordflow">if</span> (hData[i] &gt; result) {</div>
<div class="line"><a id="l00613" name="l00613"></a><span class="lineno">  613</span>                result = hData[i];</div>
<div class="line"><a id="l00614" name="l00614"></a><span class="lineno">  614</span>            }</div>
<div class="line"><a id="l00615" name="l00615"></a><span class="lineno">  615</span>        }</div>
<div class="line"><a id="l00616" name="l00616"></a><span class="lineno">  616</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00617" name="l00617"></a><span class="lineno">  617</span>    }</div>
</div>
<div class="line"><a id="l00618" name="l00618"></a><span class="lineno">  618</span> </div>
<div class="foldopen" id="foldopen00619" data-start="{" data-end="}">
<div class="line"><a id="l00619" name="l00619"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a90f7c7cde42c58b41f77d1b941da129f">  619</a></span>    Tensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a9131832f57339c0de2e7fb7955940a55">Tensor::max</a>(<span class="keyword">const</span> size_type batch, <span class="keyword">const</span> size_type channel)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00620" name="l00620"></a><span class="lineno">  620</span>        <span class="keywordflow">if</span> (batch &gt;= _shape[0] || channel &gt;= _shape[1]) {</div>
<div class="line"><a id="l00621" name="l00621"></a><span class="lineno">  621</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid position&quot;</span>);</div>
<div class="line"><a id="l00622" name="l00622"></a><span class="lineno">  622</span>        }</div>
<div class="line"><a id="l00623" name="l00623"></a><span class="lineno">  623</span>        <span class="keyword">const</span> <span class="keyword">auto</span> offset = batch * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(0) + channel * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(1);</div>
<div class="line"><a id="l00624" name="l00624"></a><span class="lineno">  624</span>        <span class="keyword">auto</span> hData = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a615af61999990e2edebacf5afbad0e57">hostData</a>();</div>
<div class="line"><a id="l00625" name="l00625"></a><span class="lineno">  625</span>        value_type result = std::numeric_limits&lt;value_type&gt;::min();</div>
<div class="line"><a id="l00626" name="l00626"></a><span class="lineno">  626</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _shape[2] * _shape[3]; ++i) {</div>
<div class="line"><a id="l00627" name="l00627"></a><span class="lineno">  627</span>            <span class="keywordflow">if</span> (hData[offset + i] &gt; result) {</div>
<div class="line"><a id="l00628" name="l00628"></a><span class="lineno">  628</span>                result = hData[offset + i];</div>
<div class="line"><a id="l00629" name="l00629"></a><span class="lineno">  629</span>            }</div>
<div class="line"><a id="l00630" name="l00630"></a><span class="lineno">  630</span>        }</div>
<div class="line"><a id="l00631" name="l00631"></a><span class="lineno">  631</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00632" name="l00632"></a><span class="lineno">  632</span>    }</div>
</div>
<div class="line"><a id="l00633" name="l00633"></a><span class="lineno">  633</span> </div>
<div class="foldopen" id="foldopen00634" data-start="{" data-end="}">
<div class="line"><a id="l00634" name="l00634"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a70caeac6652c0008b7554db438db090c">  634</a></span>    Tensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a70caeac6652c0008b7554db438db090c">Tensor::min</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00635" name="l00635"></a><span class="lineno">  635</span>        <span class="keyword">auto</span> hData = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a615af61999990e2edebacf5afbad0e57">hostData</a>();</div>
<div class="line"><a id="l00636" name="l00636"></a><span class="lineno">  636</span>        value_type result = std::numeric_limits&lt;value_type&gt;::max();</div>
<div class="line"><a id="l00637" name="l00637"></a><span class="lineno">  637</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _size; ++i) {</div>
<div class="line"><a id="l00638" name="l00638"></a><span class="lineno">  638</span>            <span class="keywordflow">if</span> (hData[i] &lt; result) {</div>
<div class="line"><a id="l00639" name="l00639"></a><span class="lineno">  639</span>                result = hData[i];</div>
<div class="line"><a id="l00640" name="l00640"></a><span class="lineno">  640</span>            }</div>
<div class="line"><a id="l00641" name="l00641"></a><span class="lineno">  641</span>        }</div>
<div class="line"><a id="l00642" name="l00642"></a><span class="lineno">  642</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00643" name="l00643"></a><span class="lineno">  643</span>    }</div>
</div>
<div class="line"><a id="l00644" name="l00644"></a><span class="lineno">  644</span> </div>
<div class="foldopen" id="foldopen00645" data-start="{" data-end="}">
<div class="line"><a id="l00645" name="l00645"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ae846233848b4cd26181205a594c083b5">  645</a></span>    Tensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a70caeac6652c0008b7554db438db090c">Tensor::min</a>(<span class="keyword">const</span> size_type batch, <span class="keyword">const</span> size_type channel)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00646" name="l00646"></a><span class="lineno">  646</span>        <span class="keywordflow">if</span> (batch &gt;= _shape[0] || channel &gt;= _shape[1]) {</div>
<div class="line"><a id="l00647" name="l00647"></a><span class="lineno">  647</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid position&quot;</span>);</div>
<div class="line"><a id="l00648" name="l00648"></a><span class="lineno">  648</span>        }</div>
<div class="line"><a id="l00649" name="l00649"></a><span class="lineno">  649</span>        <span class="keyword">const</span> <span class="keyword">auto</span> offset = batch * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(0) + channel * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(1);</div>
<div class="line"><a id="l00650" name="l00650"></a><span class="lineno">  650</span>        <span class="keyword">auto</span> hData = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a615af61999990e2edebacf5afbad0e57">hostData</a>();</div>
<div class="line"><a id="l00651" name="l00651"></a><span class="lineno">  651</span>        value_type result = std::numeric_limits&lt;value_type&gt;::max();</div>
<div class="line"><a id="l00652" name="l00652"></a><span class="lineno">  652</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _shape[2] * _shape[3]; ++i) {</div>
<div class="line"><a id="l00653" name="l00653"></a><span class="lineno">  653</span>            <span class="keywordflow">if</span> (hData[offset + i] &lt; result) {</div>
<div class="line"><a id="l00654" name="l00654"></a><span class="lineno">  654</span>                result = hData[offset + i];</div>
<div class="line"><a id="l00655" name="l00655"></a><span class="lineno">  655</span>            }</div>
<div class="line"><a id="l00656" name="l00656"></a><span class="lineno">  656</span>        }</div>
<div class="line"><a id="l00657" name="l00657"></a><span class="lineno">  657</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00658" name="l00658"></a><span class="lineno">  658</span>    }</div>
</div>
<div class="line"><a id="l00659" name="l00659"></a><span class="lineno">  659</span> </div>
<div class="foldopen" id="foldopen00660" data-start="{" data-end="}">
<div class="line"><a id="l00660" name="l00660"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a373a517d4a813c94a820d0a45806693e">  660</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a373a517d4a813c94a820d0a45806693e">Tensor::find</a>(<span class="keyword">const</span> value_type value)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00661" name="l00661"></a><span class="lineno">  661</span>        <span class="keyword">auto</span> hData = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a615af61999990e2edebacf5afbad0e57">hostData</a>();</div>
<div class="line"><a id="l00662" name="l00662"></a><span class="lineno">  662</span>        <span class="keyword">auto</span> index = 0;</div>
<div class="line"><a id="l00663" name="l00663"></a><span class="lineno">  663</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _size; ++i) {</div>
<div class="line"><a id="l00664" name="l00664"></a><span class="lineno">  664</span>            <span class="keywordflow">if</span> (hData[i] == value) {</div>
<div class="line"><a id="l00665" name="l00665"></a><span class="lineno">  665</span>                index = i;</div>
<div class="line"><a id="l00666" name="l00666"></a><span class="lineno">  666</span>                <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00667" name="l00667"></a><span class="lineno">  667</span>            }</div>
<div class="line"><a id="l00668" name="l00668"></a><span class="lineno">  668</span>        }</div>
<div class="line"><a id="l00669" name="l00669"></a><span class="lineno">  669</span>        <span class="keyword">auto</span> n = index / (_shape[1] * _shape[2] * _shape[3]);</div>
<div class="line"><a id="l00670" name="l00670"></a><span class="lineno">  670</span>        <span class="keyword">auto</span> c = (index % (_shape[1] * _shape[2] * _shape[3])) / (_shape[2] * _shape[3]);</div>
<div class="line"><a id="l00671" name="l00671"></a><span class="lineno">  671</span>        <span class="keyword">auto</span> h = (index % (_shape[2] * _shape[3])) / _shape[3];</div>
<div class="line"><a id="l00672" name="l00672"></a><span class="lineno">  672</span>        <span class="keyword">auto</span> w = index % _shape[3];</div>
<div class="line"><a id="l00673" name="l00673"></a><span class="lineno">  673</span>        <span class="keywordflow">return</span> {n, c, h, w};</div>
<div class="line"><a id="l00674" name="l00674"></a><span class="lineno">  674</span>    }</div>
</div>
<div class="line"><a id="l00675" name="l00675"></a><span class="lineno">  675</span> </div>
<div class="foldopen" id="foldopen00676" data-start="{" data-end="}">
<div class="line"><a id="l00676" name="l00676"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ae6c6bc33a47e23ec62e6a62e5e25a8ed">  676</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a373a517d4a813c94a820d0a45806693e">Tensor::find</a>(value_type value, size_type batch, size_type channel)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00677" name="l00677"></a><span class="lineno">  677</span>        <span class="keywordflow">if</span> (batch &gt;= _shape[0] || channel &gt;= _shape[1]) {</div>
<div class="line"><a id="l00678" name="l00678"></a><span class="lineno">  678</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid position&quot;</span>);</div>
<div class="line"><a id="l00679" name="l00679"></a><span class="lineno">  679</span>        }</div>
<div class="line"><a id="l00680" name="l00680"></a><span class="lineno">  680</span>        <span class="keyword">auto</span> hData = <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a615af61999990e2edebacf5afbad0e57">hostData</a>();</div>
<div class="line"><a id="l00681" name="l00681"></a><span class="lineno">  681</span>        <span class="keyword">auto</span> index = 0;</div>
<div class="line"><a id="l00682" name="l00682"></a><span class="lineno">  682</span>        <span class="keyword">auto</span> offset = batch * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(0) + channel * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(1);</div>
<div class="line"><a id="l00683" name="l00683"></a><span class="lineno">  683</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _shape[2] * _shape[3]; ++i) {</div>
<div class="line"><a id="l00684" name="l00684"></a><span class="lineno">  684</span>            <span class="keywordflow">if</span> (hData[offset + i] == value) {</div>
<div class="line"><a id="l00685" name="l00685"></a><span class="lineno">  685</span>                index = i;</div>
<div class="line"><a id="l00686" name="l00686"></a><span class="lineno">  686</span>                <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00687" name="l00687"></a><span class="lineno">  687</span>            }</div>
<div class="line"><a id="l00688" name="l00688"></a><span class="lineno">  688</span>        }</div>
<div class="line"><a id="l00689" name="l00689"></a><span class="lineno">  689</span>        <span class="keyword">auto</span> h = index / _shape[3];</div>
<div class="line"><a id="l00690" name="l00690"></a><span class="lineno">  690</span>        <span class="keyword">auto</span> w = index % _shape[3];</div>
<div class="line"><a id="l00691" name="l00691"></a><span class="lineno">  691</span>        <span class="keywordflow">return</span> {batch, channel, h, w};</div>
<div class="line"><a id="l00692" name="l00692"></a><span class="lineno">  692</span>    }</div>
</div>
<div class="line"><a id="l00693" name="l00693"></a><span class="lineno">  693</span> </div>
<div class="foldopen" id="foldopen00694" data-start="{" data-end="}">
<div class="line"><a id="l00694" name="l00694"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#aa1818a10415337403d43aad091a5a4c7">  694</a></span>    Tensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#aa1818a10415337403d43aad091a5a4c7">Tensor::expSum</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00695" name="l00695"></a><span class="lineno">  695</span>        <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00696" name="l00696"></a><span class="lineno">  696</span>        <span class="keyword">const</span> dim3 grid((_size + block.x - 1) / block.x);</div>
<div class="line"><a id="l00697" name="l00697"></a><span class="lineno">  697</span>        value_type* dData;</div>
<div class="line"><a id="l00698" name="l00698"></a><span class="lineno">  698</span>        <span class="keyword">auto</span>* hData = <span class="keyword">new</span> value_type[grid.x];</div>
<div class="line"><a id="l00699" name="l00699"></a><span class="lineno">  699</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;dData, grid.x * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00700" name="l00700"></a><span class="lineno">  700</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#a51a5ff3c8cc2c3051fddf32de294b467">krnl::SummationExp</a>(grid, block, block.x / WARP_SIZE * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), dData, _data, _size);</div>
<div class="line"><a id="l00701" name="l00701"></a><span class="lineno">  701</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(hData, dData, grid.x * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00702" name="l00702"></a><span class="lineno">  702</span>                                                             cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00703" name="l00703"></a><span class="lineno">  703</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(hData);</div>
<div class="line"><a id="l00704" name="l00704"></a><span class="lineno">  704</span>        value_type result = 0;</div>
<div class="line"><a id="l00705" name="l00705"></a><span class="lineno">  705</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; grid.x; ++i) {</div>
<div class="line"><a id="l00706" name="l00706"></a><span class="lineno">  706</span>            result += hData[i];</div>
<div class="line"><a id="l00707" name="l00707"></a><span class="lineno">  707</span>        }</div>
<div class="line"><a id="l00708" name="l00708"></a><span class="lineno">  708</span>        <span class="keyword">delete</span>[] hData;</div>
<div class="line"><a id="l00709" name="l00709"></a><span class="lineno">  709</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(dData);</div>
<div class="line"><a id="l00710" name="l00710"></a><span class="lineno">  710</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00711" name="l00711"></a><span class="lineno">  711</span>    }</div>
</div>
<div class="line"><a id="l00712" name="l00712"></a><span class="lineno">  712</span> </div>
<div class="foldopen" id="foldopen00713" data-start="{" data-end="}">
<div class="line"><a id="l00713" name="l00713"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#ac4833838e9a704b6b8d29cbd53c6b3b1">  713</a></span>    Tensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#aa1818a10415337403d43aad091a5a4c7">Tensor::expSum</a>(<span class="keyword">const</span> <span class="keywordtype">size_t</span> batch, <span class="keyword">const</span> <span class="keywordtype">size_t</span> channel)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00714" name="l00714"></a><span class="lineno">  714</span>        <span class="keywordflow">if</span> (batch &gt;= _shape[0] || channel &gt;= _shape[1]) {</div>
<div class="line"><a id="l00715" name="l00715"></a><span class="lineno">  715</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid position&quot;</span>);</div>
<div class="line"><a id="l00716" name="l00716"></a><span class="lineno">  716</span>        }</div>
<div class="line"><a id="l00717" name="l00717"></a><span class="lineno">  717</span>        <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> = _shape[2] * _shape[3];</div>
<div class="line"><a id="l00718" name="l00718"></a><span class="lineno">  718</span>        <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00719" name="l00719"></a><span class="lineno">  719</span>        <span class="keyword">const</span> dim3 grid((<a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a> + block.x - 1) / block.x);</div>
<div class="line"><a id="l00720" name="l00720"></a><span class="lineno">  720</span>        value_type* dData;</div>
<div class="line"><a id="l00721" name="l00721"></a><span class="lineno">  721</span>        <span class="keyword">auto</span>* hData = <span class="keyword">new</span> value_type[grid.x];</div>
<div class="line"><a id="l00722" name="l00722"></a><span class="lineno">  722</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;dData, grid.x * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00723" name="l00723"></a><span class="lineno">  723</span>        <span class="keyword">const</span> <span class="keyword">auto</span> offset = batch * _shape.getStride(0) + channel * _shape.getStride(1);</div>
<div class="line"><a id="l00724" name="l00724"></a><span class="lineno">  724</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#a51a5ff3c8cc2c3051fddf32de294b467">krnl::SummationExp</a>(grid, block, block.x / WARP_SIZE * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), dData, _data, <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">size</a>, offset);</div>
<div class="line"><a id="l00725" name="l00725"></a><span class="lineno">  725</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(hData, dData, grid.x * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00726" name="l00726"></a><span class="lineno">  726</span>                                                             cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00727" name="l00727"></a><span class="lineno">  727</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(hData);</div>
<div class="line"><a id="l00728" name="l00728"></a><span class="lineno">  728</span>        value_type result = 0;</div>
<div class="line"><a id="l00729" name="l00729"></a><span class="lineno">  729</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; grid.x; ++i) {</div>
<div class="line"><a id="l00730" name="l00730"></a><span class="lineno">  730</span>            result += hData[i];</div>
<div class="line"><a id="l00731" name="l00731"></a><span class="lineno">  731</span>        }</div>
<div class="line"><a id="l00732" name="l00732"></a><span class="lineno">  732</span>        <span class="keyword">delete</span>[] hData;</div>
<div class="line"><a id="l00733" name="l00733"></a><span class="lineno">  733</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(dData);</div>
<div class="line"><a id="l00734" name="l00734"></a><span class="lineno">  734</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00735" name="l00735"></a><span class="lineno">  735</span>    }</div>
</div>
<div class="line"><a id="l00736" name="l00736"></a><span class="lineno">  736</span> </div>
<div class="foldopen" id="foldopen00737" data-start="{" data-end="}">
<div class="line"><a id="l00737" name="l00737"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a7aab89d371ff013c5c021a191bd7348e">  737</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a7aab89d371ff013c5c021a191bd7348e">Tensor::syncData</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00738" name="l00738"></a><span class="lineno">  738</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(_data);</div>
<div class="line"><a id="l00739" name="l00739"></a><span class="lineno">  739</span>    }</div>
</div>
<div class="line"><a id="l00740" name="l00740"></a><span class="lineno">  740</span> </div>
<div class="foldopen" id="foldopen00741" data-start="{" data-end="}">
<div class="line"><a id="l00741" name="l00741"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#af28425ddc9bee1f75fd923a0de68c37b">  741</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#af28425ddc9bee1f75fd923a0de68c37b">Tensor::syncGrad</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00742" name="l00742"></a><span class="lineno">  742</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00743" name="l00743"></a><span class="lineno">  743</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(_grad);</div>
<div class="line"><a id="l00744" name="l00744"></a><span class="lineno">  744</span>        }</div>
<div class="line"><a id="l00745" name="l00745"></a><span class="lineno">  745</span>    }</div>
</div>
<div class="line"><a id="l00746" name="l00746"></a><span class="lineno">  746</span> </div>
<div class="foldopen" id="foldopen00747" data-start="{" data-end="}">
<div class="line"><a id="l00747" name="l00747"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_tensor.html#a0c150b841f02921eb7826a6e03d0267e">  747</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a0c150b841f02921eb7826a6e03d0267e">Tensor::sync</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00748" name="l00748"></a><span class="lineno">  748</span>        <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#a7aab89d371ff013c5c021a191bd7348e">syncData</a>();</div>
<div class="line"><a id="l00749" name="l00749"></a><span class="lineno">  749</span>        <a class="code hl_function" href="classnz_1_1data_1_1_tensor.html#af28425ddc9bee1f75fd923a0de68c37b">syncGrad</a>();</div>
<div class="line"><a id="l00750" name="l00750"></a><span class="lineno">  750</span>    }</div>
</div>
<div class="line"><a id="l00751" name="l00751"></a><span class="lineno">  751</span>}</div>
<div class="ttc" id="a_operation_kernels_8cuh_html"><div class="ttname"><a href="_operation_kernels_8cuh.html">OperationKernels.cuh</a></div><div class="ttdoc">CUDA Kernel Definitions for High-Performance Tensor Operations.</div></div>
<div class="ttc" id="a_tensor_8cuh_html"><div class="ttname"><a href="_tensor_8cuh.html">Tensor.cuh</a></div><div class="ttdoc">Definition of the Tensor class for GPU-based tensor operations.</div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html">nz::cuStrm::StreamManager</a></div><div class="ttdoc">Centralized CUDA stream and resource management system with automatic dependency tracking.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00131">StreamManager.cuh:131</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_a1084057ef6f5b2871c60702209bb4469"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#a1084057ef6f5b2871c60702209bb4469">nz::cuStrm::StreamManager::freeAsync</a></div><div class="ttdeci">void freeAsync(T *data)</div><div class="ttdoc">Asynchronously frees the CUDA device memory pointed to by the given pointer.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00325">StreamManager.cuh:325</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_a71ad766cb2869d3dd6a3931966e81706"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#a71ad766cb2869d3dd6a3931966e81706">nz::cuStrm::StreamManager::memset</a></div><div class="ttdeci">void memset(T *data, const int value, const size_t count)</div><div class="ttdoc">Asynchronously sets a block of CUDA device memory to a specified value.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00360">StreamManager.cuh:360</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_a731986c2c4ecd056562eaddadef46df8"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#a731986c2c4ecd056562eaddadef46df8">nz::cuStrm::StreamManager::randomize</a></div><div class="ttdeci">void randomize(T *data, size_t size, size_t seed, curandRngType_t rngType)</div><div class="ttdoc">Generates uniformly distributed random numbers on GPU using CURAND.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00757">StreamManager.cuh:757</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_a785cf34395067f425e032d9bd5e1fa20"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">nz::cuStrm::StreamManager::free</a></div><div class="ttdeci">void free(T *data)</div><div class="ttdoc">Frees the CUDA device memory pointed to by the given pointer.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00263">StreamManager.cuh:263</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_a97f78a2d43f6e0508c82d4f3b629de96"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">nz::cuStrm::StreamManager::malloc</a></div><div class="ttdeci">void malloc(T **data, const size_t size)</div><div class="ttdoc">Asynchronously allocates device memory for type-specific data with stream-ordered dependency tracking...</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00230">StreamManager.cuh:230</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_ab4b2eb422e0e1ee44bdfdc0eb94457ce"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">nz::cuStrm::StreamManager::Instance</a></div><div class="ttdeci">static StreamManager &amp; Instance()</div><div class="ttdoc">Returns a reference to the singleton instance of the StreamManager.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00154">StreamManager.cuh:154</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_abe439fa00c0bd369c0b2345b095ed5af"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">nz::cuStrm::StreamManager::syncData</a></div><div class="ttdeci">void syncData(T *data)</div><div class="ttdoc">Synchronizes host thread with completion events for a specific data object.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00714">StreamManager.cuh:714</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_afa38d5c6db0e6b48c8f74ce8ad0df2bc"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">nz::cuStrm::StreamManager::memcpy</a></div><div class="ttdeci">void memcpy(T *dst, T *src, const size_t size, const cudaMemcpyKind kind)</div><div class="ttdoc">Asynchronously copies data between CUDA device and host memory based on the specified memory copy kin...</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00391">StreamManager.cuh:391</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html">nz::data::Dimension</a></div><div class="ttdoc">Represents a multi - dimensional shape, typically used in deep learning for tensor dimensions.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cuh_source.html#l00057">Dimension.cuh:57</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_a073622bb031999163987ccf77f8edfb2"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#a073622bb031999163987ccf77f8edfb2">nz::data::Dimension::size</a></div><div class="ttdeci">size_t size() const</div><div class="ttdoc">Calculates the total number of elements in the Dimension object.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00036">Dimension.cu:36</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_a4831fea5aaf7dbad3578d3fa8e55aef1"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">nz::data::Dimension::getStride</a></div><div class="ttdeci">size_t getStride(size_t i) const</div><div class="ttdoc">Retrieves the stride value at a specified index within the Dimension object.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00040">Dimension.cu:40</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_a65773c675476dfea3f06b30f21ebbedd"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#a65773c675476dfea3f06b30f21ebbedd">nz::data::Dimension::W</a></div><div class="ttdeci">size_t W() const</div><div class="ttdoc">Retrieves the value of the 'w' dimension.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00063">Dimension.cu:63</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_a7eb3acc882c48e775c418d97f709240f"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#a7eb3acc882c48e775c418d97f709240f">nz::data::Dimension::H</a></div><div class="ttdeci">size_t H() const</div><div class="ttdoc">Retrieves the value of the 'h' dimension.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00059">Dimension.cu:59</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_ab4f9f0cec97b8e579b62ccb37975de3c"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#ab4f9f0cec97b8e579b62ccb37975de3c">nz::data::Dimension::Broadcast</a></div><div class="ttdeci">Dimension Broadcast(const Dimension &amp;other) const</div><div class="ttdoc">Performs broadcasting between two Dimension objects and returns the resulting Dimension.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00125">Dimension.cu:125</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_acc472e84b4c44f649f34b6fbb0eeacf7"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#acc472e84b4c44f649f34b6fbb0eeacf7">nz::data::Dimension::N</a></div><div class="ttdeci">size_t N() const</div><div class="ttdoc">Retrieves the value of the 'n' dimension.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00051">Dimension.cu:51</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_ae1e87c4a462dd60e02821aa27ffc7e09"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#ae1e87c4a462dd60e02821aa27ffc7e09">nz::data::Dimension::C</a></div><div class="ttdeci">size_t C() const</div><div class="ttdoc">Retrieves the value of the 'c' dimension.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00055">Dimension.cu:55</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html">nz::data::Tensor</a></div><div class="ttdoc">A class for representing and manipulating multidimensional arrays (tensors) in GPU memory.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_8cuh_source.html#l00134">Tensor.cuh:134</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a0c150b841f02921eb7826a6e03d0267e"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a0c150b841f02921eb7826a6e03d0267e">nz::data::Tensor::sync</a></div><div class="ttdeci">void sync() const</div><div class="ttdoc">Synchronize both the tensor data and its gradient data.</div><div class="ttdef"><b>Definition</b> <a href="#l00747">Tensor.cu:747</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a178a2240cd5d441be508490b2613fc55"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a178a2240cd5d441be508490b2613fc55">nz::data::Tensor::recip</a></div><div class="ttdeci">void recip() const</div><div class="ttdoc">Computes the reciprocal (1/x) of each element in the tensor and updates the tensor in-place.</div><div class="ttdef"><b>Definition</b> <a href="#l00554">Tensor.cu:554</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a2b2309d5428331f2e6f88037bb123c8f"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a2b2309d5428331f2e6f88037bb123c8f">nz::data::Tensor::print</a></div><div class="ttdeci">std::ostream &amp; print(std::ostream &amp;os) const</div><div class="ttdoc">Prints the tensor data to an output stream.</div><div class="ttdef"><b>Definition</b> <a href="#l00252">Tensor.cu:252</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a2f9be06ac6766a5fa6de3548c722ef43"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a2f9be06ac6766a5fa6de3548c722ef43">nz::data::Tensor::setData</a></div><div class="ttdeci">void setData(const shape_type &amp;position, value_type value, bool isGrad=false) const</div><div class="ttdoc">Sets the value of an element in the tensor or its gradient at a specified position.</div><div class="ttdef"><b>Definition</b> <a href="#l00410">Tensor.cu:410</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a31a3aa01fa3ccb56503994a99e39e177"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a31a3aa01fa3ccb56503994a99e39e177">nz::data::Tensor::size</a></div><div class="ttdeci">size_type size() const noexcept</div><div class="ttdoc">Retrieves the total number of elements in the tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00226">Tensor.cu:226</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a36cd1679c45059de64deeca9152b0288"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a36cd1679c45059de64deeca9152b0288">nz::data::Tensor::operator+</a></div><div class="ttdeci">Tensor operator+(const Tensor &amp;other) const</div><div class="ttdoc">Adds two tensors element-wise and returns the result.</div><div class="ttdef"><b>Definition</b> <a href="#l00331">Tensor.cu:331</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a373a517d4a813c94a820d0a45806693e"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a373a517d4a813c94a820d0a45806693e">nz::data::Tensor::find</a></div><div class="ttdeci">shape_type find(value_type value) const</div><div class="ttdoc">Finds the first occurrence of a given value in the entire tensor and returns its shape indices.</div><div class="ttdef"><b>Definition</b> <a href="#l00660">Tensor.cu:660</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a38ba233ef49f34620297f96edd962c55"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a38ba233ef49f34620297f96edd962c55">nz::data::Tensor::data</a></div><div class="ttdeci">value_type * data() const noexcept</div><div class="ttdoc">Retrieves a pointer to the tensor's data stored in GPU memory.</div><div class="ttdef"><b>Definition</b> <a href="#l00432">Tensor.cu:432</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a45e6f84ae74111ced9a96bdf204b2294"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a45e6f84ae74111ced9a96bdf204b2294">nz::data::Tensor::transpose</a></div><div class="ttdeci">void transpose()</div><div class="ttdoc">Transposes the tensor by swapping its dimensions and rearranging the data.</div><div class="ttdef"><b>Definition</b> <a href="#l00385">Tensor.cu:385</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a4a657091dfa6a490d873ab8e95d9bb9e"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a4a657091dfa6a490d873ab8e95d9bb9e">nz::data::Tensor::sum</a></div><div class="ttdeci">value_type sum() const</div><div class="ttdoc">Compute the sum of all elements in the Tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00565">Tensor.cu:565</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a4b02ed4d2afec1ce75931201af181e14"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a4b02ed4d2afec1ce75931201af181e14">nz::data::Tensor::printGrad</a></div><div class="ttdeci">std::ostream &amp; printGrad(std::ostream &amp;os) const</div><div class="ttdoc">Prints the gradient values of the tensor to an output stream.</div><div class="ttdef"><b>Definition</b> <a href="#l00464">Tensor.cu:464</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a615af61999990e2edebacf5afbad0e57"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a615af61999990e2edebacf5afbad0e57">nz::data::Tensor::hostData</a></div><div class="ttdeci">std::vector&lt; value_type &gt; hostData() const noexcept</div><div class="ttdoc">Retrieves the tensor data from the device to the host and returns it as a std::vector.</div><div class="ttdef"><b>Definition</b> <a href="#l00436">Tensor.cu:436</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a6fed8efad540a7621dd6640b2f2466d0"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a6fed8efad540a7621dd6640b2f2466d0">nz::data::Tensor::zeroGrad</a></div><div class="ttdeci">void zeroGrad() const</div><div class="ttdoc">Resets the gradient data to zero.</div><div class="ttdef"><b>Definition</b> <a href="#l00246">Tensor.cu:246</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a70caeac6652c0008b7554db438db090c"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a70caeac6652c0008b7554db438db090c">nz::data::Tensor::min</a></div><div class="ttdeci">value_type min() const</div><div class="ttdoc">Finds the minimum value in the entire tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00634">Tensor.cu:634</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a7a9f1d5fae2989181645e5f59f7666d8"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a7a9f1d5fae2989181645e5f59f7666d8">nz::data::Tensor::randomize</a></div><div class="ttdeci">void randomize(unsigned long long seed=0) const</div><div class="ttdoc">Randomizes the tensor's data with a uniform distribution.</div><div class="ttdef"><b>Definition</b> <a href="#l00298">Tensor.cu:298</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a7aab89d371ff013c5c021a191bd7348e"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a7aab89d371ff013c5c021a191bd7348e">nz::data::Tensor::syncData</a></div><div class="ttdeci">void syncData() const</div><div class="ttdoc">Synchronize the tensor data by waiting for all CUDA stream write operations to complete.</div><div class="ttdef"><b>Definition</b> <a href="#l00737">Tensor.cu:737</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a7cbc6dd248b882c95840835d0deaae1c"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a7cbc6dd248b882c95840835d0deaae1c">nz::data::Tensor::requiresGrad</a></div><div class="ttdeci">bool requiresGrad() const noexcept</div><div class="ttdoc">Checks whether the tensor requires gradient computation.</div><div class="ttdef"><b>Definition</b> <a href="#l00224">Tensor.cu:224</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a7fd4badf84f9c5398e08b23a9826dfbc"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a7fd4badf84f9c5398e08b23a9826dfbc">nz::data::Tensor::hostGrad</a></div><div class="ttdeci">std::vector&lt; value_type &gt; hostGrad() const</div><div class="ttdoc">Retrieves the gradient data of the tensor from the device to the host and returns it as a std::vector...</div><div class="ttdef"><b>Definition</b> <a href="#l00452">Tensor.cu:452</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a877f9f2704e39100142d81d289ddc3f2"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a877f9f2704e39100142d81d289ddc3f2">nz::data::Tensor::reshape</a></div><div class="ttdeci">void reshape(const shape_type &amp;shape)</div><div class="ttdoc">Reshapes the tensor to the specified shape.</div><div class="ttdef"><b>Definition</b> <a href="#l00358">Tensor.cu:358</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a9131832f57339c0de2e7fb7955940a55"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a9131832f57339c0de2e7fb7955940a55">nz::data::Tensor::max</a></div><div class="ttdeci">value_type max() const</div><div class="ttdoc">Finds the maximum value in the tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00608">Tensor.cu:608</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a92c7313608326bb4123d6f08341a6d80"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a92c7313608326bb4123d6f08341a6d80">nz::data::Tensor::operator==</a></div><div class="ttdeci">bool operator==(const Tensor &amp;other) const</div><div class="ttdoc">Checks if two Tensor objects are equal.</div><div class="ttdef"><b>Definition</b> <a href="#l00506">Tensor.cu:506</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_a98a8b254d2b6c8b4893d7a286452a9b0"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#a98a8b254d2b6c8b4893d7a286452a9b0">nz::data::Tensor::~Tensor</a></div><div class="ttdeci">~Tensor() noexcept(false)</div><div class="ttdoc">Destructor for Tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00210">Tensor.cu:210</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_aa1818a10415337403d43aad091a5a4c7"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#aa1818a10415337403d43aad091a5a4c7">nz::data::Tensor::expSum</a></div><div class="ttdeci">value_type expSum() const</div><div class="ttdoc">Compute the sum of the exponential values of all elements in the Tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00694">Tensor.cu:694</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_aaa22ac6f3de75ee92a4307320eda7e87"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#aaa22ac6f3de75ee92a4307320eda7e87">nz::data::Tensor::operator*</a></div><div class="ttdeci">Tensor operator*(const Tensor &amp;other) const</div><div class="ttdoc">Performs matrix multiplication of two tensors (matrices) and returns the result.</div><div class="ttdef"><b>Definition</b> <a href="#l00343">Tensor.cu:343</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_aade7b0c42622279888d755f4f7989aac"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#aade7b0c42622279888d755f4f7989aac">nz::data::Tensor::shape</a></div><div class="ttdeci">shape_type shape() const noexcept</div><div class="ttdoc">Retrieves the shape of the tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00225">Tensor.cu:225</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_aae7b7714f78f4d366e66f1664d37d36a"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#aae7b7714f78f4d366e66f1664d37d36a">nz::data::Tensor::operator!=</a></div><div class="ttdeci">bool operator!=(const Tensor &amp;other) const</div><div class="ttdoc">Checks if two Tensor objects are not equal.</div><div class="ttdef"><b>Definition</b> <a href="#l00550">Tensor.cu:550</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_abddb47a6dc305d289a1e4f91d01a5082"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#abddb47a6dc305d289a1e4f91d01a5082">nz::data::Tensor::setRequiresGrad</a></div><div class="ttdeci">void setRequiresGrad(bool requires_grad)</div><div class="ttdoc">Sets whether the tensor requires gradient computation.</div><div class="ttdef"><b>Definition</b> <a href="#l00229">Tensor.cu:229</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_acdb68bf53d38e5a93fdd0effa4c3059a"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#acdb68bf53d38e5a93fdd0effa4c3059a">nz::data::Tensor::operator=</a></div><div class="ttdeci">Tensor &amp; operator=(const Tensor &amp;other)</div><div class="ttdoc">Assignment operator for Tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00173">Tensor.cu:173</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_ad0dda0efff93778cab46fd5aa708b983"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#ad0dda0efff93778cab46fd5aa708b983">nz::data::Tensor::Tensor</a></div><div class="ttdeci">Tensor()</div><div class="ttdoc">Default constructor for Tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00088">Tensor.cu:88</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_ad220de56b18c404611f07f2290cd7e9d"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#ad220de56b18c404611f07f2290cd7e9d">nz::data::Tensor::fill</a></div><div class="ttdeci">void fill(value_type value, bool isGrad=false) const</div><div class="ttdoc">Fills the tensor's data with a specified value.</div><div class="ttdef"><b>Definition</b> <a href="#l00306">Tensor.cu:306</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_ad6107b98beb881d0209345185d5ad145"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#ad6107b98beb881d0209345185d5ad145">nz::data::Tensor::grad</a></div><div class="ttdeci">value_type * grad() const</div><div class="ttdoc">Retrieves a pointer to the gradient data stored in GPU memory.</div><div class="ttdef"><b>Definition</b> <a href="#l00445">Tensor.cu:445</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_ad66d0c0f5d9ecb375e1006bc0aecf404"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#ad66d0c0f5d9ecb375e1006bc0aecf404">nz::data::Tensor::operator-</a></div><div class="ttdeci">Tensor operator-() const</div><div class="ttdoc">Negates all elements of the tensor and returns the result.</div><div class="ttdef"><b>Definition</b> <a href="#l00498">Tensor.cu:498</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_ad6ac34675276afe1fb2ee2f5d16af538"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#ad6ac34675276afe1fb2ee2f5d16af538">nz::data::Tensor::operator/</a></div><div class="ttdeci">Tensor operator/(const Tensor &amp;other) const</div><div class="ttdoc">Performs element-wise division between two Tensors.</div><div class="ttdef"><b>Definition</b> <a href="#l00352">Tensor.cu:352</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_adf80894b8e06f260bb2695951e2f539e"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#adf80894b8e06f260bb2695951e2f539e">nz::data::Tensor::dataInject</a></div><div class="ttdeci">void dataInject(value_type *data, bool grad=false) const</div><div class="ttdoc">Injects data or gradient data into the tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00282">Tensor.cu:282</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_ae6144f6d7fa612d98538f17baf4ef574"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#ae6144f6d7fa612d98538f17baf4ef574">nz::data::Tensor::fillMatrix</a></div><div class="ttdeci">void fillMatrix(value_type value, size_type batch, size_type channels, bool isGrad=false)</div><div class="ttdoc">Fill a specific matrix slice within the Tensor with a given value.</div><div class="ttdef"><b>Definition</b> <a href="#l00316">Tensor.cu:316</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_af28425ddc9bee1f75fd923a0de68c37b"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#af28425ddc9bee1f75fd923a0de68c37b">nz::data::Tensor::syncGrad</a></div><div class="ttdeci">void syncGrad() const</div><div class="ttdoc">Synchronize the gradient data of the tensor if gradient computation is required.</div><div class="ttdef"><b>Definition</b> <a href="#l00741">Tensor.cu:741</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html_afc4e6385b97cf7ceb8bb74748b73b681"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html#afc4e6385b97cf7ceb8bb74748b73b681">nz::data::Tensor::clear</a></div><div class="ttdeci">void clear() const</div><div class="ttdoc">Clears the tensor's data by setting all elements to zero.</div><div class="ttdef"><b>Definition</b> <a href="#l00302">Tensor.cu:302</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html"><div class="ttname"><a href="namespacenz_1_1data.html">nz::data</a></div><div class="ttdoc">Contains data structures and utilities for tensor operations in machine learning workflows.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cuh_source.html#l00009">Dimension.cuh:9</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_a1da5cd018533919ed5a750b14c7d6d71"><div class="ttname"><a href="namespacenz_1_1data.html#a1da5cd018533919ed5a750b14c7d6d71">nz::data::tensorElementwiseDivide</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, void &gt; tensorElementwiseDivide(T &amp;out, const T &amp;lhs, const T &amp;rhs)</div><div class="ttdoc">Performs element - wise division operation on tensors with broadcast compatibility.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l00928">TensorOperations.cuh:928</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_a5a166a472b887c45fde9e5815f072234"><div class="ttname"><a href="namespacenz_1_1data.html#a5a166a472b887c45fde9e5815f072234">nz::data::tensorGeneralMatrixMul</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, void &gt; tensorGeneralMatrixMul(T &amp;out, const T &amp;lhs, const T &amp;rhs)</div><div class="ttdoc">Performs general matrix multiplication on tensors with broadcast compatibility.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l01000">TensorOperations.cuh:1000</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_a7503b6894e8052ed54eb169550d135c0"><div class="ttname"><a href="namespacenz_1_1data.html#a7503b6894e8052ed54eb169550d135c0">nz::data::tensorMatrixSub</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, void &gt; tensorMatrixSub(T &amp;out, const T &amp;lhs, const T &amp;rhs)</div><div class="ttdoc">Performs matrix subtraction operation on tensors with broadcast compatibility.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l00858">TensorOperations.cuh:858</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_a8cf4ac2437dd67698684169bebb225d4"><div class="ttname"><a href="namespacenz_1_1data.html#a8cf4ac2437dd67698684169bebb225d4">nz::data::tensorMatrixAdd</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, void &gt; tensorMatrixAdd(T &amp;out, const T &amp;lhs, const T &amp;rhs)</div><div class="ttdoc">Performs matrix addition operation on tensors with broadcast compatibility.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l00787">TensorOperations.cuh:787</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a1ae846a65c2f5b83cd1b9fc61b877854"><div class="ttname"><a href="namespacenz_1_1krnl.html#a1ae846a65c2f5b83cd1b9fc61b877854">nz::krnl::Summation</a></div><div class="ttdeci">void Summation(dim3 gridDim, dim3 blockDim, unsigned long long sharedMemSize, float *out, float *in, unsigned long long n, size_t offset=0)</div><div class="ttdoc">Kernel function to perform element-wise summation of two arrays.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l01225">OperationKernels.cu:1225</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a51a5ff3c8cc2c3051fddf32de294b467"><div class="ttname"><a href="namespacenz_1_1krnl.html#a51a5ff3c8cc2c3051fddf32de294b467">nz::krnl::SummationExp</a></div><div class="ttdeci">void SummationExp(dim3 gridDim, dim3 blockDim, size_t sharedMemSize, float *out, float *g_data, unsigned long long n, size_t offset=0)</div><div class="ttdoc">Kernel function to compute the summation of exponentials of each element in the input array.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00510">OperationKernels.cu:510</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_ad136c8a6560a5305984ce0a31bea71bf"><div class="ttname"><a href="namespacenz_1_1krnl.html#ad136c8a6560a5305984ce0a31bea71bf">nz::krnl::Fill</a></div><div class="ttdeci">void Fill(dim3 gridDim, dim3 blockDim, float *data, float value, unsigned long long n, size_t offset=0)</div><div class="ttdoc">Kernel function to fill a data array with a given value.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l01153">OperationKernels.cu:1153</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_adc047e65307dbc711235f637227b7d10"><div class="ttname"><a href="namespacenz_1_1krnl.html#adc047e65307dbc711235f637227b7d10">nz::krnl::Recip</a></div><div class="ttdeci">void Recip(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n)</div><div class="ttdoc">Kernel function to compute the reciprocal of each element of a matrix on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00226">OperationKernels.cu:226</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_af7069a420e81babb49b1bc009333d053"><div class="ttname"><a href="namespacenz_1_1krnl.html#af7069a420e81babb49b1bc009333d053">nz::krnl::Negation</a></div><div class="ttdeci">void Negation(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n)</div><div class="ttdoc">Kernel function to negate each element of a matrix on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00209">OperationKernels.cu:209</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_afe3f38f788c735b7eb718443eb0fd094"><div class="ttname"><a href="namespacenz_1_1krnl.html#afe3f38f788c735b7eb718443eb0fd094">nz::krnl::Transpose</a></div><div class="ttdeci">void Transpose(dim3 gridDim, dim3 blockDim, float *d_A, float *d_B, unsigned int rows, unsigned int cols, size_t offset=0)</div><div class="ttdoc">Kernel function to transpose a matrix on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00147">OperationKernels.cu:147</a></div></div>
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